Meetings: Documents

A Fully Internally Calibrated Aquarius Brightness Temperature Measurement Using Instrument-Only Corrections
[13-Nov-2014] Misra, S. and Brown, S.
Presented at the 2014 Aquarius/SAC-D Science Team Meeting
The Aquarius mission was launched in June 2011 and has been successfully providing global sea-surface salinity (SSS) measurements at a very high fidelity. The L-band radiometer is calibrated using a common internal noise-diode system between the V-pol and H-pol channels, two separate internal noise-diodes for each channel, as well as a dicke-load. The calibrated brightness temperature is then de-biased with respect to an ocean salinity model to achieve absolute calibration.
Since launch the Aquarius radiometer has observed a slow drift over many months, as well as a pseudo-periodic signal ("wiggles") that is inconsistent between the 6 channels.
Based on measurements observed over modeled ocean, Antarctic and Amazon regions, it is determined that the drift observed is a ?gain' drift and the wiggles observed is an ?offset' issue. The current drift calibration correction applies an exponential correction to the internal noise-diode values to correct for the gain drift. The exponential and offset correction is derived off the difference between measured Tbs and modeled Tb for each channel. Thus the calibration correction is model dependent.
As noted above, there is a need for an Aquarius drift and wiggle correction that is not dependent on the ocean salinity model. The following talk will present an instrument-only calibration algorithm that does not use the ocean salinity model for calibration correction. The talk is divided in 3 portions. The first part will discuss the potential root-cause for the offset and gain drifts observed. The histogram of uncalibrated counts that point towards the root-cause of the wiggles and drift. The spikes observed are a result of frequency-locking at the backend of the radiometer. The locking causes the radiometer to get stuck at certain brightness temperature values more than its neighboring values. This adds in a skewness to the calibrated data set due to a slow reference load drift and results in wiggles.
The second portion of the talk details the wiggle correction algorithm applied. The correction algorithm takes advantage of the fact that two consecutive reference load counts have a slight bias with respect to each other and pass through locked count regions at different times. This allows us to implement a differential boot-strapping algorithm, that can correct for offset wiggles based on an known initial condition.
The last portion of the talk will discuss the drift correction algorithm. Looking at other data values such as reference+noise diode, antenna+noise-diode it is observed that the gain drift is not due to a noise-diode drift. The gain drift is associated with a backend issue, similar to the frequency locking issue that causes wiggles. Looking at noise-diode deflection ratios between antenna and reference load, we fit and apply a gain correction to the data. This fit does not use any information from the ocean salinity model.
Further work implementing the above algorithms towards a version 4.0 Aquarius data set will also be discussed.

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